OpenRaiser/Intern-Atlas huggingface dataset:
A Comparison of Classifiers in Performing Speaker Accent Recognition Using MFCCs
Venue: arXiv cs.SD 2015
Year: 2015

An algorithm involving Mel-Frequency Cepstral Coefficients (MFCCs) is provided to perform signal feature extraction for the task of speaker accent recognition. Then different classifiers are compared based on the MFCC feature. For each signal, the mean vector of MFCC matrix is used as an input vector for pattern recognition. A sample of 330 signals, containing 165 US voice and 165 non-US voice, is analyzed. By comparison, k-nearest neighbors yield the highest average test accuracy, after using a cross-validation of size 500, and least time being used in the computation
Optimal offline virtual network embedding with rent-at-bulk aspects
Venue: arXiv cs.NI 2015
Year: 2015

Network virtualization techniques allow for the coexistence of many virtual networks (VNs) jointly sharing the resources of an underlying substrate network. The Virtual Network Embedding problem (VNE) arises when looking for the most profitable set of VNs to embed onto the substrate. In this paper, we address the offline version of the problem. We propose a Mixed-Integer Linear Programming formulation to solve it to optimality which accounts for acceptance and rejection of virtual network requests, allowing for both splittable and unsplittable (single path) routing schemes. Our formulation also considers a Rent-at-Bulk (RaB) model for the rental of substrate capacities where economies of scale apply. To better emphasize the importance of RaB, we also compare our method to a baseline one which only takes RaB into account a posteriori, once a solution to VNE, oblivious to RaB, has been found. Computational experiments show the viability of our approach, stressing the relevance of addressing RaB directly with an exact formulation
Domain-Type-Guided Refinement Selection Based on Sliced Path Prefixes
Venue: arXiv cs.SE 2015
Year: 2015

Abstraction is a successful technique in software verification, and interpolation on infeasible error paths is a successful approach to automatically detect the right level of abstraction in counterexample-guided abstraction refinement. Because the interpolants have a significant influence on the quality of the abstraction, and thus, the effectiveness of the verification, an algorithm for deriving the best possible interpolants is desirable. We present an analysis-independent technique that makes it possible to extract several alternative sequences of interpolants from one given infeasible error path, if there are several reasons for infeasibility in the error path. We take as input the given infeasible error path and apply a slicing technique to obtain a set of error paths that are more abstract than the original error path but still infeasible, each for a different reason. The (more abstract) constraints of the new paths can be passed to a standard interpolation engine, in order to obtain a set of interpolant sequences, one for each new path. The analysis can then choose from this set of interpolant sequences and select the most appropriate, instead of being bound to the single interpolant sequence that the interpolation engine would normally return. For example, we can select based on domain types of variables in the interpolants, prefer to avoid loop counters, or compare with templates for potential loop invariants, and thus control what kind of information occurs in the abstraction of the program. We implemented the new algorithm in the open-source verification framework CPAchecker and show that our proof-technique-independent approach yields a significant improvement of the effectiveness and efficiency of the verification process.
Byzantine Broadcast Under a Selective Broadcast Model for Single-hop Wireless Networks
Venue: arXiv cs.DC 2015
Year: 2015

This paper explores an old problem, {\em Byzantine fault-tolerant Broadcast} (BB), under a new model, {\em selective broadcast model}. The new model "interpolates" between the two traditional models in the literature. In particular, it allows fault-free nodes to exploit the benefits of a broadcast channel (a feature from reliable broadcast model) and allows faulty nodes to send mismatching messages to different neighbors (a feature from point-to-point model) simultaneously. The {\em selective broadcast} model is motivated by the potential for {\em directional} transmissions on a wireless channel. We provide a collection of results for a single-hop wireless network under the new model. First, we present an algorithm for {\em Multi-Valued} BB that is order-optimal in bit complexity. Then, we provide an algorithm that is designed to achieve BB efficiently in terms of message complexity. Third, we determine some lower bounds on both bit and message complexities of BB problems in the {\em selective broadcast model}. Finally, we present a conjecture on an "exact" lower bound on the bit complexity of BB under the {\em selective broadcast} model.
Category-Epitomes : Discriminatively Minimalist Representations for Object Categories
Venue: arXiv cs.CV 2015
Year: 2015

Freehand line sketches are an interesting and unique form of visual representation. Typically, such sketches are studied and utilized as an end product of the sketching process. However, we have found it instructive to study the sketches as sequentially accumulated composition of drawing strokes added over time. Studying sketches in this manner has enabled us to create novel sparse yet discriminative sketch-based representations for object categories which we term category-epitomes. Our procedure for obtaining these epitomes concurrently provides a natural measure for quantifying the sparseness underlying the original sketch, which we term epitome-score. We construct and analyze category-epitomes and epitome-scores for freehand sketches belonging to various object categories. Our analysis provides a novel viewpoint for studying the semantic nature of object categories.
Optimized Projection for Sparse Representation Based Classification
Venue: arXiv cs.CV 2015
Year: 2015

Dimensionality reduction (DR) methods have been commonly used as a principled way to understand the high-dimensional data such as facial images. In this paper, we propose a new supervised DR method called Optimized Projection for Sparse Representation based Classification (OP-SRC), which is based on the recent face recognition method, Sparse Representation based Classification (SRC). SRC seeks a sparse linear combination on all the training data for a given query image, and make the decision by the minimal reconstruction residual. OP-SRC is designed on the decision rule of SRC, it aims to reduce the within-class reconstruction residual and simultaneously increase the between-class reconstruction residual on the training data. The projections are optimized and match well with the mechanism of SRC. Therefore, SRC performs well in the OP-SRC transformed space. The feasibility and effectiveness of the proposed method is verified on the Yale, ORL and UMIST databases with promising results.
Hybrid Radio/Free-Space Optical Design for Next Generation Backhaul Systems
Venue: arXiv cs.IT 2015
Year: 2015

The deluge of date rate in today's networks imposes a cost burden on the backhaul network design. Developing cost efficient backhaul solutions becomes an exciting, yet challenging, problem. Traditional technologies for backhaul networks include either radio-frequency backhauls (RF) or optical fibers (OF). While RF is a cost-effective solution as compared to OF, it supports lower data rate requirements. Another promising backhaul solution is the free-space optics (FSO) as it offers both a high data rate and a relatively low cost. FSO, however, is sensitive to nature conditions, e.g., rain, fog, line-of-sight. This paper combines both RF and FSO advantages and proposes a hybrid RF/FSO backhaul solution. It considers the problem of minimizing the cost of the backhaul network by choosing either OF or hybrid RF/FSO backhaul links between the base-stations (BS) so as to satisfy data rate, connectivity, and reliability constraints. It shows that under a specified realistic assumption about the cost of OF and hybrid RF/FSO links, the problem is equivalent to a maximum weight clique problem, which can be solved with moderate complexity. Simulation results show that the proposed solution shows a close-to-optimal performance, especially for practical prices of the hybrid RF/FSO links.
An in-between "implicit" and "explicit" complexity: Automata
Venue: arXiv cs.LO 2015
Year: 2015

Implicit Computational Complexity makes two aspects implicit, by manipulating programming languages rather than models of com-putation, and by internalizing the bounds rather than using external measure. We survey how automata theory contributed to complexity with a machine-dependant with implicit bounds model.
An Inter-molecular Adaptive Collision Scheme for Chemical Reaction Optimization
Venue: arXiv cs.NE 2015
Year: 2015

Optimization techniques are frequently applied in science and engineering research and development. Evolutionary algorithms, as a kind of general-purpose metaheuristic, have been shown to be very effective in solving a wide range of optimization problems. A recently proposed chemical-reaction-inspired metaheuristic, Chemical Reaction Optimization (CRO), has been applied to solve many global optimization problems. However, the functionality of the inter-molecular ineffective collision operator in the canonical CRO design overlaps that of the on-wall ineffective collision operator, which can potential impair the overall performance. In this paper we propose a new inter-molecular ineffective collision operator for CRO for global optimization. To fully utilize our newly proposed operator, we also design a scheme to adapt the algorithm to optimization problems with different search space characteristics. We analyze the performance of our proposed algorithm with a number of widely used benchmark functions. The simulation results indicate that the new algorithm has superior performance over the canonical CRO.
Chemical Reaction Optimization for the Set Covering Problem
Venue: arXiv cs.NE 2015
Year: 2015

The set covering problem (SCP) is one of the representative combinatorial optimization problems, having many practical applications. This paper investigates the development of an algorithm to solve SCP by employing chemical reaction optimization (CRO), a general-purpose metaheuristic. It is tested on a wide range of benchmark instances of SCP. The simulation results indicate that this algorithm gives outstanding performance compared with other heuristics and metaheuristics in solving SCP.
Base Station Switching Problem for Green Cellular Networks with Social Spider Algorithm
Venue: arXiv cs.NI 2015
Year: 2015

With the recent explosion in mobile data, the energy consumption and carbon footprint of the mobile communications industry is rapidly increasing. It is critical to develop more energy-efficient systems in order to reduce the potential harmful effects to the environment. One potential strategy is to switch off some of the under-utilized base stations during off-peak hours. In this paper, we propose a binary Social Spider Algorithm to give guidelines for selecting base stations to switch off. In our implementation, we use a penalty function to formulate the problem and manage to bypass the large number of constraints in the original optimization problem. We adopt several randomly generated cellular networks for simulation and the results indicate that our algorithm can generate superior performance.
Fountain Uncorrectable Sets and Finite-Length Analysis
Venue: arXiv cs.IT 2015
Year: 2015

Decoding performance of Fountain codes for the binary erasure channel (BEC) depends on two aspects. One is the essential code structure, on which stopping set analysis operates. The other is the effect from the channel characteristic, which is difficult to give a precise estimation. To tackle these problems, in this paper, we propose a solution to analyzing the performance of Fountain codes based on the uncorrectable set. We give the condition for Fountain decoding failure over the BEC. Then, we conduct the analysis of uncorrectable set on Fountain codes. Finally, we combine the stopping set and the uncorrectable set to provide the integrated analysis on the performance of Fountain codes for BEC.
Dynamic texture and scene classification by transferring deep image features
Venue: arXiv cs.CV 2015
Year: 2015

Dynamic texture and scene classification are two fundamental problems in understanding natural video content. Extracting robust and effective features is a crucial step towards solving these problems. However the existing approaches suffer from the sensitivity to either varying illumination, or viewpoint changing, or even camera motion, and/or the lack of spatial information. Inspired by the success of deep structures in image classification, we attempt to leverage a deep structure to extract feature for dynamic texture and scene classification. To tackle with the challenges in training a deep structure, we propose to transfer some prior knowledge from image domain to video domain. To be specific, we propose to apply a well-trained Convolutional Neural Network (ConvNet) as a mid-level feature extractor to extract features from each frame, and then form a representation of a video by concatenating the first and the second order statistics over the mid-level features. We term this two-level feature extraction scheme as a Transferred ConvNet Feature (TCoF). Moreover we explore two different implementations of the TCoF scheme, i.e., the \textit{spatial} TCoF and the \textit{temporal} TCoF, in which the mean-removed frames and the difference between two adjacent frames are used as the inputs of the ConvNet, respectively. We evaluate systematically the proposed spatial TCoF and the temporal TCoF schemes on three benchmark data sets, including DynTex, YUPENN, and Maryland, and demonstrate that the proposed approach yields superior performance.
On the Accelerating of Two-dimensional Smart Laplacian Smoothing on the GPU
Venue: arXiv cs.DC 2015
Year: 2015

This paper presents a GPU-accelerated implementation of two-dimensional Smart Laplacian smoothing. This implementation is developed under the guideline of our paradigm for accelerating Laplacianbased mesh smoothing [13]. Two types of commonly used data layouts, Array-of-Structures (AoS) and Structure-of-Arrays (SoA) are used to represent triangular meshes in our implementation. Two iteration forms that have different choices of the swapping of intermediate data are also adopted. Furthermore, the feature CUDA Dynamic Parallelism (CDP) is employed to realize the nested parallelization in Smart Laplacian smoothing. Experimental results demonstrate that: (1) our implementation can achieve the speedups of up to 44x on the GPU GT640; (2) the data layout AoS can always obtain better efficiency than the SoA layout; (3) the form that needs to swap intermediate nodal coordinates is always slower than the one that does not swap data; (4) the version of our implementation with the use of the feature CDP is slightly faster than the version where the CDP is not adopted.
Codes for DNA Sequence Profiles
Venue: arXiv cs.IT 2015
Year: 2015

We consider the problem of storing and retrieving information from synthetic DNA media. The mathematical basis of the problem is the construction and design of sequences that may be discriminated based on their collection of substrings observed through a noisy channel. This problem of reconstructing sequences from traces was first investigated in the noiseless setting under the name of "Markov type" analysis. Here, we explain the connection between the reconstruction problem and the problem of DNA synthesis and sequencing, and introduce the notion of a DNA storage channel. We analyze the number of sequence equivalence classes under the channel mapping and propose new asymmetric coding techniques to combat the effects of synthesis and sequencing noise. In our analysis, we make use of restricted de Bruijn graphs and Ehrhart theory for rational polytopes.
Optimal Two-Tier Forecasting Power Generation Model in Smart Grids
Venue: arXiv cs.SY 2015
Year: 2015

There has been an increasing trend in the electric power system from a centralized generation-driven grid to a more reliable, environmental friendly, and customer-driven grid. One of the most important issues which the designers of smart grids need to deal with is to forecast the fluctuations of power demand and generation in order to make the power system facilities more flexible to the variable nature of renewable power resources and demand-side. This paper proposes a novel two-tier scheme for forecasting the power demand and generation in a general residential electrical gird which uses the distributed renewable resources as the primary energy resource. The proposed forecasting scheme has two tiers: long-term demand/generation forecaster which is based on Maximum-Likelihood Estimator (MLE) and real-time demand/generation forecaster which is based on Auto-Regressive Integrated Moving-Average (ARIMA) model. The paper also shows that how bulk generation improves the adequacy of proposed residential system by canceling-out the forecasters estimation errors which are in the form of Gaussian White noises.
Beyond Pixels: A Comprehensive Survey from Bottom-up to Semantic Image Segmentation and Cosegmentation
Venue: arXiv cs.CV 2015
Year: 2015

Image segmentation refers to the process to divide an image into nonoverlapping meaningful regions according to human perception, which has become a classic topic since the early ages of computer vision. A lot of research has been conducted and has resulted in many applications. However, while many segmentation algorithms exist, yet there are only a few sparse and outdated summarizations available, an overview of the recent achievements and issues is lacking. We aim to provide a comprehensive review of the recent progress in this field. Covering 180 publications, we give an overview of broad areas of segmentation topics including not only the classic bottom-up approaches, but also the recent development in superpixel, interactive methods, object proposals, semantic image parsing and image cosegmentation. In addition, we also review the existing influential datasets and evaluation metrics. Finally, we suggest some design flavors and research directions for future research in image segmentation.
Learning Contour-Fragment-based Shape Model with And-Or Tree Representation
Venue: arXiv cs.CV 2015
Year: 2015

This paper proposes a simple yet effective method to learn the hierarchical object shape model consisting of local contour fragments, which represents a category of shapes in the form of an And-Or tree. This model extends the traditional hierarchical tree structures by introducing the "switch" variables (i.e. the or-nodes) that explicitly specify production rules to capture shape variations. We thus define the model with three layers: the leaf-nodes for detecting local contour fragments, the or-nodes specifying selection of leaf-nodes, and the root-node encoding the holistic distortion. In the training stage, for optimization of the And-Or tree learning, we extend the concave-convex procedure (CCCP) by embedding the structural clustering during the iterative learning steps. The inference of shape detection is consistent with the model optimization, which integrates the local testings via the leaf-nodes and or-nodes with the global verification via the root-node. The advantages of our approach are validated on the challenging shape databases (i.e., ETHZ and INRIA Horse) and summarized as follows. (1) The proposed method is able to accurately localize shape contours against unreliable edge detection and edge tracing. (2) The And-Or tree model enables us to well capture the intraclass variance.
Deep Joint Task Learning for Generic Object Extraction
Venue: arXiv cs.CV 2015
Year: 2015

This paper investigates how to extract objects-of-interest without relying on hand-craft features and sliding windows approaches, that aims to jointly solve two sub-tasks: (i) rapidly localizing salient objects from images, and (ii) accurately segmenting the objects based on the localizations. We present a general joint task learning framework, in which each task (either object localization or object segmentation) is tackled via a multi-layer convolutional neural network, and the two networks work collaboratively to boost performance. In particular, we propose to incorporate latent variables bridging the two networks in a joint optimization manner. The first network directly predicts the positions and scales of salient objects from raw images, and the latent variables adjust the object localizations to feed the second network that produces pixelwise object masks. An EM-type method is presented for the optimization, iterating with two steps: (i) by using the two networks, it estimates the latent variables by employing an MCMC-based sampling method; (ii) it optimizes the parameters of the two networks unitedly via back propagation, with the fixed latent variables. Extensive experiments suggest that our framework significantly outperforms other state-of-the-art approaches in both accuracy and efficiency (e.g. 1000 times faster than competing approaches).
Recognizing Focal Liver Lesions in Contrast-Enhanced Ultrasound with Discriminatively Trained Spatio-Temporal Model
Venue: arXiv cs.CV 2015
Year: 2015

The aim of this study is to provide an automatic computational framework to assist clinicians in diagnosing Focal Liver Lesions (FLLs) in Contrast-Enhancement Ultrasound (CEUS). We represent FLLs in a CEUS video clip as an ensemble of Region-of-Interests (ROIs), whose locations are modeled as latent variables in a discriminative model. Different types of FLLs are characterized by both spatial and temporal enhancement patterns of the ROIs. The model is learned by iteratively inferring the optimal ROI locations and optimizing the model parameters. To efficiently search the optimal spatial and temporal locations of the ROIs, we propose a data-driven inference algorithm by combining effective spatial and temporal pruning. The experiments show that our method achieves promising results on the largest dataset in the literature (to the best of our knowledge), which we have made publicly available.
Big Data: Opportunities and Privacy Challenges
Venue: arXiv cs.CR 2015
Year: 2015

Recent advances in data collection and computational statistics coupled with increases in computer processing power, along with the plunging costs of storage are making technologies to effectively analyze large sets of heterogeneous data ubiquitous. Applying such technologies (often referred to as big data technologies) to an ever growing number and variety of internal and external data sources, businesses and institutions can discover hidden correlations between data items, and extract actionable insights needed for innovation and economic growth. While on one hand big data technologies yield great promises, on the other hand, they raise critical security, privacy, and ethical issues, which if left unaddressed may become significant barriers to the fulfillment of expected opportunities and long-term success of big data. In this paper, we discuss the benefits of big data to individuals and society at large, focusing on seven key use cases: Big data for business optimization and customer analytics, big data and science, big data and health care, big data and finance, big data and the emerging energy distribution systems, big/open data as enablers of openness and efficiency in government, and big data security. In addition to benefits and opportunities, we discuss the security, privacy, and ethical issues at stake.